Volume 14 | Issue 4 |

 Abstract

Road traffic accidents are a problem that causes deaths and bad injuries all over the world. When Road traffic accidents happen it is really important that people get help away. The old way of watching CCTV cameras is not very good because it mostly relies on people watching them and this can be very slow. The Smart Traffic Accident Detection and Alert System is a way to do things. It uses computer programs to look at CCTV pictures and find Road traffic accidents. This system uses a Convolutional Neural Network to figure out if a Road traffic accident happened. It also uses a YOLO-based object detection model to find the car and draw a box around it. When the Smart Traffic Accident Detection and Alert System thinks a Road traffic accident happened and it is pretty sure it writes down what happened and tells a person to check. The person has 30 seconds to look at it through a web page. If the person does not do anything the Smart Traffic Accident Detection and Alert System will send out emergency messages again and again. The Smart Traffic Accident Detection and Alert System is a way to help people who get hurt in Road traffic accidents. It can find Road traffic accidents quickly. It can get help to people who need it. The Smart Traffic Accident Detection and Alert System is an improvement, over the way of watching CCTV cameras.

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 Keywords

Smart traffic systems; accident detection; deep learning; YOLO; computer vision; intelligent transportation systems (ITS); emergency response automation; real-time surveillance; metadata extraction; cloud integration; Flask-based deployment; multi-camera monitoring; decision tagging; analytics dashboard; road safety.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Many small businesses face difficulty in moving to digital platforms. The travel and tourism industry is one of the areas which could benefit from utilizing digital transformation to improve both customer reach as well as the efficiency of providing services. This research paper outlines both the design and development of a new user centered and scalable web application for tour and travel companies. This system will allow small business owners to handle all aspects of their business including managing customer's inquiries and bookings via a single web based interface. The application has been built using HTML, CSS, and JavaScript on the front end and has Firebase for managing authentication (signing up) and databases, while EmailJS has also been implemented to provide email confirmation of bookings and inquiries. With the newly developed application, users have the capability to sign up, sign in, browse through travel packages, and make reservations respectively in a fast and efficient manner. The reservations are stored in an encrypted format and can be retrieved in real time by business owners. This research paper provides a concise and functional application for small business owners that have a formalized structure supporting the digitalization of their operations. As a result, the user experience is enhanced, manual operations are reduced and overall business efficiency is markedly increased. This proposed solution is also scalable and contains the capability of additional modifications such as payment systems and administrative dashboards to be added in the future

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 Keywords

Web Application, Travel Website, Firebase, EmailJS, User Centric Design, Digital Business

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 Abstract

Invasive fungal infections (IFI) are now recognized as a serious global health threats placing a heavy burden on health care system. Treating thus infections is difficult because the available antifungal drugs often cause side effects , have limited effectiveness and face rising resistance. Over the past 30 years,researchers have carried out extensive testing of antifungal drugs.These efforts have been crucial in creating treatment guidelines for serious fungal infections. Most of these guidelines are based on well known and highly cited clinical trials. While we have made progress in developing antifungal medicines that are more effective and less toxic than older options .Early detections and targeted treatment This growing challenge highlights the urgent need for new antifungal medications. This review explores the latest advancements in antifungal drug development, focusing on innovative compounds that are either being tested in clinical trials or are likely to be evaluated soon. These new agents stand out because of their novel mechanisms of action, broader antimicrobial coverage, and improved pharmacokinetic properties. Together, these qualities hold promise for more effective treatment options than traditional drugs.

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 Keywords

: Invasive fungal infections (IFIs), Global health concern, Drug resistance, Clinical trials, Pharmacokinetics, Improved patient outcomes

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 Abstract

The proliferation of deepfake videos synthesized through deep generative models such as Generative Adversarial Networks (GANs) and neural autoencoders poses critical threats to digital security, media integrity, political stability, and public trust. Existing detection approaches either focus exclusively on spatial artifacts within individual frames using CNNs, or on temporal inconsistencies using recurrent networks, but rarely integrate both. This paper presents a novel hybrid deep learning framework combining Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) for robust, video-level deepfake detection. The proposed system employs EfficientNetB0 as the spatial feature extractor to capture per- frame facial artifacts, while a two-layer Bidirectional LSTM models temporal inconsistencies across a sequence of 20 uniformly sampled frames. Face regions are detected using the OpenCV Haar Cascade detector with 25% bounding box padding. The model is trained and evaluated on the FaceForensics++ benchmark comprising 7,000 videos across six manipulation categories: Deepfakes, Face2Face, FaceShifter, FaceSwap, NeuralTextures, and DeepFakeDetection. Class imbalance (1:5 real-to-fake ratio) is addressed through Weighted Random Sampling, label smoothing, and stratified splitting. Training employs AdamW optimization, Cosine Annealing scheduling, and PyTorch Automatic Mixed Precision on an NVIDIA Tesla T4 GPU. Experimental evaluation yields a test accuracy of 92.4%, F1-Score of 91.7%, and AUC- ROC of 0.967, demonstrating the superiority of joint spatial- temporal learning over frame-only and temporal-only approaches. A Flask-based real-time web application is developed for practical deployment, providing binary REAL / FAKE verdicts with confidence scores and REST API integration for downstream systems.

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 Keywords

Deepfake Detection, CNN-LSTM, EfficientNetB0, Bidirectional LSTM, FaceForensics++, Video Forensics, Temporal Analysis, Face Manipulation, GAN, Deep Learning, OpenCV, Flask, Real-Time Detection

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study explores motivational HR practices in Mumbai startups and the contextual factors shaping their adoption. Drawing on pilot survey data (N=35) and secondary data from 661 Mumbai startups, the analysis identifies flexible work arrangements, regular recognition, and career growth communication as the most prevalent practices (mean scores 4.00-4.26). Conversely, equity-based incentives such as ESOPs exhibit relatively lower adoption (mean 2.91), indicating a cautious approach to ownership dilution among early-stage ventures. Contextual analysis highlights high operational costs, intense competition for skilled talent, and funding constraints as key ecosystem-level drivers of HR design choices. Bootstrapped startups (42.9 percent) display a greater reliance on non-monetary and relational motivators compared with venture-funded firms that can complement these with financial incentives. With a 71.4 percent active survival rate in the pilot sample, the findings suggest that strategically aligned motivational HR practices constitute an important lever for startup resilience and sustainability in Mumbai's dynamic but demanding environment.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Motivational HR practices, startup survival, Mumbai ecosystem, employee retention, entrepreneurial HRM

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The advent of COVID-19 created an unprecedented divide, forcing a radical re-evaluation of this centuries-old tradition. With the closure of holy shrines and strict social distancing protocols, the traditional narrative of physical pilgrimage became impossible. This led to a profound shift, giving rise to new concepts of spiritual practice. The pandemic accelerated the adoption of technology, giving birth to the "virtual pilgrimage," where devotees could participate in online darshans and live-streamed aartis. This decoupling of the spiritual experience from a physical location is a key change. Furthermore, the enforced isolation encouraged a focus on "interior pilgrimage," a more personal and introspective journey of faith. This paper explores the contrasting pilgrimage narratives across these two phases: the pre-pandemic era, defined by physical presence and community, and the post-pandemic landscape, characterised by a hybrid model of spiritual practice. By analysing these narratives, this study aims to illuminate how Tirth Yatra has adapted, demonstrating the resilience of faith in the face of modern challenges and highlighting the new dimensions of spirituality brought about by connectivity, communication, technology, and introspection.

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 Keywords

Keywords: Tirth Yatra, Pilgrimage, COVID-19, Virtual Darshan, Spiritual Narratives, Hybrid Faith, Indian Temples

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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Agro-waste fibers, Natural dyes, Mordanting techniques, Eco-friendly home decor, Sustainable craftsmanship, Value-added products

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Military supply chain logistics in complex and contested environments depends critically on timely, accurate terrain assessment and adaptive route planning. Traditional methods relying on manual reconnaissance or satellite optical imagery often fall short in adverse weather and low-visibility conditions. This paper presents an integrated Explainable Ar- tificial Intelligence (XAI) framework that fuses Sentinel-1 (S1) Synthetic Aperture Radar (SAR) and Sentinel-2 (S2) multispec- tral satellite imagery for automated terrain classification and intelligent military route optimization. A lightweight Convolu- tional Neural Network (CNN) is trained to classify terrain into four operationally relevant categories--urban, grassland, barren- land, and agricultural--with confidence estimation. Gradient- weighted Class Activation Mapping (Grad-CAM) is applied to provide pixel-level visual explanations of the model's decisions, supporting transparency and trust in mission-critical deploy- ments. Route safety is scored dynamically based on terrain type across three candidate paths, and an integrated Large Language Model (Qwen2.5) generates natural-language military logistics justifications. The complete system is deployed as a web-based interactive application built using Django, Bootstrap, HTML5, CSS3, JavaScript, and SQLite3. Experimental results on the OpenSARUrban and SEN12 datasets demonstrate 97.4% terrain classification accuracy alongside meaningful and actionable route recommendations. The proposed framework advances trustwor- thy AI for defense logistics and sets a foundation for real-time battlefield supply chain intelligence.

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 Keywords

Synthetic Aperture Radar, Explainable AI, Grad-CAM, Military Supply Chain, Terrain Classification, Con- volutional Neural Network, Route Optimization, Sentinel-1, Sentinel-2, Large Language Models, Django, Deep Learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Social media platforms such as Instagram have experienced rapid growth over the past decade. However, this expansion has also introduced significant risks, including fake profiles, impersonation attacks, spam conversations, and phishing attempts. Malicious actors frequently exploit deceptive profile information and social engineering methods, making it genuinely difficult for ordinary users to spot fraudulent accounts. Existing detection solutions are mostly controlled by the platforms them- selves and operate as closed, opaque systems that offer very little transparency or direct control to individual users.

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 Keywords

Fake account detection, Instagram authenticity, machine learning, TF-IDF, Random Forest, XGBoost, Naive Bayes, heuristic analysis, social media security, risk scoring.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The ancient Uttarapatha, meaning the "Northern Road," represents one of the earliest extensive trade networks in the Indian subcontinent, serving as a vital artery for interaction between South Asia, Central Asia, and the wider Eurasian region. Predating the widespread recognition of the Silk Route, this corridor facilitated connections among prominent urban centres, including Taxila, Mathura, Varanasi, and Pataliputra. Through these linkages, it enabled not only commercial exchange but also the movement of ideas, cultures, and religious traditions.

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 Keywords

Uttarapatha, Silk Route, Ancient Indian Trade Routes, Trans-Asian Trade Networks, Cultural Exchange, Central Asia, Connectivity, Economic History.

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Creative Commons Attribution 4.0 and The Open Definition